Added: practice exercise 1 from getting started

1 files changed, 79 insertions, 0 deletions

diff --git a/mike_getting_started.py b/mike_getting_started.py
index e69de29..3729bc1 100644
--- a/mike_getting_started.py
+++ b/mike_getting_started.py
@@ -0,0 +1,79 @@
+import numpy as np
+
+credit_data = np.genfromtxt('./credit_score.txt', delimiter=',', skip_header=True)
+
+# "credit_data" is now a 2d NumPy array. Each rows represent a record and the
+# columns represent the data attributes.
+# [(22, 0, 0, 28, 1, 0) 
+#  (46, 0, 1, 32, 0, 0) 
+#  (24, 1, 1, 24, 1, 0)
+#  (25, 0, 0, 27, 1, 0) 
+#  (29, 1, 1, 32, 0, 0) 
+#  (45, 1, 1, 30, 0, 1)
+#  (63, 1, 1, 58, 1, 1) 
+#  (36, 1, 0, 52, 1, 1) 
+#  (23, 0, 1, 40, 0, 1)
+#  (50, 1, 1, 28, 0, 1)]
+
+# print(credit_data)
+
+first_row = credit_data[0]
+# print('The first row: ', first_row)
+
+fourth_col = credit_data[:,3]
+# print('The fourth column: ', fourth_col)
+
+# print(credit_data[...,1:])
+
+# print(credit_data[:3,2])
+
+# print(np.sort(np.unique(credit_data[:,3])))
+
+# print('Total number of examples with binary label 1:', np.sum(credit_data[:,5]))
+
+# print('Sum of all entries in the cols:', credit_data.sum(axis=0))
+
+# print('Select all rows where the first column is bigger than 27:', credit_data[credit_data[:,0] > 27])
+
+x = np.array([2,5,10])
+# print(x)
+
+# print(np.arange(0, 10))
+
+# Select the *row numbers* of the rows where the first column of credit_data is bigger than 27:
+# print(np.arange(0,10)[credit_data[:,0] > 27])
+
+# Draw a random sample of size 5 from the numbers 1 through 10 (without replacement):
+index = np.random.choice(np.arange(0,10), size=5, replace=False)
+# print(index)
+
+train = credit_data[index,]
+# print(train)
+
+test = np.delete(credit_data, index, axis=0)
+# print(test)
+
+# help(np.random.choice)
+
+# Practice exercise1
+
+# test_array = credit_data[:,-1]
+test_array = np.array([1,0,1,1,1,0,0,1,1,0,1])
+def impurity(array) -> None:
+    """
+    @todo: Docstring for 
+    """
+    # print(array)
+    rel_freq_1_len = len(test_array[0:])
+    print('len of the vector:', rel_freq_1_len)
+    rel_freq_1_sum = test_array[0:].sum()
+    print(rel_freq_1_sum)
+    rel_freq_1 = rel_freq_1_sum / rel_freq_1_len
+    rel_freq_0 = 1 - rel_freq_1
+    print('\nThe rel. freq. of 1: ', rel_freq_1)
+    print('\nThe rel. freq. of 0: ', rel_freq_0)
+    gini_index = rel_freq_1 * rel_freq_0
+    print('\nThe gini index: ', gini_index)
+    # pass
+
+impurity(test_array)